WeChat Mini Program
Old Version Features

    • An Improved Switching Method-Based Diagnostic Strategy for IGBT Open-Circuit Faults in Hybrid Modular Multilevel Converters

    Hong Wu,Yue Wang,Yi Liu,Yonghui Liu,Yufei Li

    IEEE TRANSACTIONS ON POWER ELECTRONICS（2025）

    Xi An Jiao Tong Univ

    Cited 0|Views7
    Abstract
    The hybrid modular multilevel converter (HMMC) has attracted extensive attention due to its dc fault ride-through capability. However, the research on its insulated gate bipolar transistor (IGBT) open-circuit fault (OCF) diagnosis is still barely seen, which becomes a research gap that may jeopardize the reliability of the HMMC system. To address this issue, this article first proposes an improved switching method, which can ensure all six types of IGBT OCFs unveil their fault characteristics thoroughly by using an extra bypassed switching sequence. Furthermore, the improved switching method remains the same switching loss as the existing switching method. Afterward, a diagnostic method is brought forward to identify the faulty SM by its fixed specific sequence in a period of time while the switching sequences of other SMs with the same type are dynamic. On this basis, an improved switching method-based diagnostic strategy that consists of various types of diagnostic methods is proposed, which can diagnose all six types of IGBT OCFs in the HMMC within 20 ms and have immunity to dc faults and load change. Experimental results in a hardware-in-the-loop platform verify the effectiveness of the proposed switching method and diagnostic strategy.
    More
    Translated text
    Key words
    Diagnostic strategy,hybrid modular multilevel converters,open-circuit fault,switching loss,switching method,Diagnostic strategy,hybrid modular multilevel converters,open-circuit fault,switching loss,switching method
    求助PDF
    上传PDF
    View via Publisher
    Bibtex
    AI Read Science
    AI Summary
    AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
    Example
    Background
    Key content
    Introduction
    Methods
    Results
    Related work
    Fund
    Key content
    • Pretraining has recently greatly promoted the development of natural language processing (NLP)
    • We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
    • We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
    • The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
    • Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
    Upload PDF to Generate Summary
    Must-Reading Tree
    Example
    Generate MRT to find the research sequence of this paper
    Data Disclaimer
    The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
    Chat Paper
    Summary is being generated by the instructions you defined